{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:24:35Z","timestamp":1775690675492,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NSFC 11871290"],"award-info":[{"award-number":["NSFC 11871290"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873185"],"award-info":[{"award-number":["61873185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianjin Graduate Research and Innovation Project","award":["2019YJSB043"],"award-info":[{"award-number":["2019YJSB043"]}]},{"name":"KLMDASR"},{"DOI":"10.13039\/501100004806","name":"Fok Ying-Tong Education Foundation","doi-asserted-by":"crossref","award":["161003"],"award-info":[{"award-number":["161003"]}],"id":[{"id":"10.13039\/501100004806","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Recent years have witnessed that the inter-residue contact\/distance in proteins could be accurately predicted by deep neural networks, which significantly improve the accuracy of predicted protein structure models. In contrast, fewer studies have been done for the prediction of RNA inter-nucleotide 3D closeness.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We proposed a new algorithm named RNAcontact for the prediction of RNA inter-nucleotide 3D closeness. RNAcontact was built based on the deep residual neural networks. The covariance information from multiple sequence alignments and the predicted secondary structure were used as the input features of the networks. Experiments show that RNAcontact achieves the respective precisions of 0.8 and 0.6 for the top L\/10 and L (where L is the length of an RNA) predictions on an independent test set, significantly higher than other evolutionary coupling methods. Analysis shows that about 1\/3 of the correctly predicted 3D closenesses are not base pairings of secondary structure, which are critical to the determination of RNA structure. In addition, we demonstrated that the predicted 3D closeness could be used as distance restraints to guide RNA structure folding by the 3dRNA package. More accurate models could be built by using the predicted 3D closeness than the models without using 3D closeness.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The webserver and a standalone package are available at: http:\/\/yanglab.nankai.edu.cn\/RNAcontact\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa932","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T11:10:48Z","timestamp":1603365048000},"page":"1093-1098","source":"Crossref","is-referenced-by-count":35,"title":["RNA inter-nucleotide 3D closeness prediction by deep residual neural networks"],"prefix":"10.1093","volume":"37","author":[{"given":"Saisai","family":"Sun","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}]},{"given":"Wenkai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}]},{"given":"Zhenling","family":"Peng","sequence":"additional","affiliation":[{"name":"Center for Applied Mathematics, Tianjin University , Tianjin 300072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2912-7737","authenticated-orcid":false,"given":"Jianyi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Nankai University , Tianjin 300071, China"}]}],"member":"286","published-online":{"date-parts":[[2020,12,10]]},"reference":[{"key":"2023051612090554600_btaa932-B1","first-page":"265","author":"Abadi","year":"2016"},{"key":"2023051612090554600_btaa932-B2","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1002\/prot.25787","article-title":"A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments","volume":"87","author":"Abriata","year":"2019","journal-title":"Proteins"},{"key":"2023051612090554600_btaa932-B3","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023051612090554600_btaa932-B4","doi-asserted-by":"crossref","first-page":"737","DOI":"10.18388\/abp.2016_1329","article-title":"New functionality of RNAComposer: an application to shape the axis of miR160 precursor structure","volume":"63","author":"Antczak","year":"2017","journal-title":"Acta Biochim. Pol"},{"key":"2023051612090554600_btaa932-B5","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2023051612090554600_btaa932-B6","doi-asserted-by":"crossref","first-page":"e63","DOI":"10.1093\/nar\/gkv1479","article-title":"SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction","volume":"44","author":"Boniecki","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023051612090554600_btaa932-B7","first-page":"10444","article-title":"Direct-coupling analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction","volume":"43","author":"De Leonardis","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023051612090554600_btaa932-B8","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/978-1-4939-2291-8_19","article-title":"The ViennaRNA web services","volume":"1269","author":"Gruber","year":"2015","journal-title":"Methods Mol. Biol"},{"key":"2023051612090554600_btaa932-B9","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1186\/s12859-019-3099-4","article-title":"DIRECT: RNA contact predictions by integrating structural patterns","volume":"20","author":"Jian","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2023051612090554600_btaa932-B10","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1261\/rna.1270809","article-title":"Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters","volume":"15","author":"Jonikas","year":"2009","journal-title":"RNA"},{"key":"2023051612090554600_btaa932-B11","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1002\/prot.25779","article-title":"Prediction of interresidue contacts with DeepMetaPSICOV in CASP13","volume":"87","author":"Kandathil","year":"2019","journal-title":"Proteins"},{"key":"2023051612090554600_btaa932-B12","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.1093\/bioinformatics\/btv221","article-title":"iFoldRNA v2: folding RNA with constraints","volume":"31","author":"Krokhotin","year":"2015","journal-title":"Bioinformatics"},{"key":"2023051612090554600_btaa932-B13","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1017\/S1355838201002515","article-title":"Geometric nomenclature and classification of RNA base pairs","volume":"7","author":"Leontis","year":"2001","journal-title":"RNA"},{"key":"2023051612090554600_btaa932-B14","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/978-3-642-25740-7_13","volume-title":"RNA 3D Structure Analysis and Prediction","author":"Leontis","year":"2012"},{"key":"2023051612090554600_btaa932-B15","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"2023051612090554600_btaa932-B16","doi-asserted-by":"crossref","first-page":"4647","DOI":"10.1093\/bioinformatics\/btz291","article-title":"ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks","volume":"35","author":"Li","year":"2019","journal-title":"Bioinformatics"},{"key":"2023051612090554600_btaa932-B17","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1261\/rna.060368.116","article-title":"RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme","volume":"23","author":"Miao","year":"2017","journal-title":"RNA"},{"key":"2023051612090554600_btaa932-B18","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1093\/bioinformatics\/btt509","article-title":"Infernal 1.1: 100-fold faster RNA homology searches","volume":"29","author":"Nawrocki","year":"2013","journal-title":"Bioinformatics"},{"key":"2023051612090554600_btaa932-B19","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1093\/nar\/gku208","article-title":"CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction","volume":"42","author":"Puton","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023051612090554600_btaa932-B20","doi-asserted-by":"crossref","first-page":"6355","DOI":"10.1093\/nar\/gkn544","article-title":"Unifying evolutionary and thermodynamic information for RNA folding of multiple alignments","volume":"36","author":"Seemann","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2023051612090554600_btaa932-B21","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1093\/bioinformatics\/bty876","article-title":"Enhanced prediction of RNA solvent accessibility with long short-term memory neural networks and improved sequence profiles","volume":"35","author":"Sun","year":"2019","journal-title":"Bioinformatics"},{"key":"2023051612090554600_btaa932-B22","doi-asserted-by":"crossref","first-page":"6299","DOI":"10.1093\/nar\/gkx386","article-title":"Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide-nucleotide interactions from direct coupling analysis","volume":"45","author":"Wang","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023051612090554600_btaa932-B23","doi-asserted-by":"crossref","first-page":"e1005324","DOI":"10.1371\/journal.pcbi.1005324","article-title":"Accurate de novo prediction of protein contact map by ultra-deep learning model","volume":"13","author":"Wang","year":"2017","journal-title":"PLoS Comput. Biol"},{"key":"2023051612090554600_btaa932-B24","doi-asserted-by":"crossref","first-page":"4116","DOI":"10.3390\/ijms20174116","article-title":"3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction","volume":"20","year":"2019","journal-title":"Int. J. Mol. Sci."},{"key":"2023051612090554600_btaa932-B25","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1016\/j.cell.2016.03.030","article-title":"3D RNA and functional interactions from evolutionary couplings","volume":"165","author":"Weinreb","year":"2016","journal-title":"Cell"},{"key":"2023051612090554600_btaa932-B26","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1093\/bioinformatics\/btz477","article-title":"Protein contact prediction using metagenome sequence data and residual neural networks","volume":"36","author":"Wu","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051612090554600_btaa932-B27","doi-asserted-by":"crossref","first-page":"16856","DOI":"10.1073\/pnas.1821309116","article-title":"Distance-based protein folding powered by deep learning","volume":"116","author":"Xu","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051612090554600_btaa932-B28","doi-asserted-by":"crossref","first-page":"3450","DOI":"10.1093\/nar\/gkg529","article-title":"Tools for the automatic identification and classification of RNA base pairs","volume":"31","author":"Yang","year":"2003","journal-title":"Nucleic Acids Res"},{"key":"2023051612090554600_btaa932-B29","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1073\/pnas.1914677117","article-title":"Improved protein structure prediction using predicted interresidue orientations","volume":"117","author":"Yang","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa932\/34841269\/btaa932.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/8\/1093\/50340984\/btaa932.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/8\/1093\/50340984\/btaa932.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T12:10:24Z","timestamp":1684239024000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/8\/1093\/5948987"}},"subtitle":[],"editor":[{"given":"Yann","family":"Ponty","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":29,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,5,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa932","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,4,15]]},"published":{"date-parts":[[2020,12,10]]}}}